Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation

• URL: http://arxiv.org/abs/2104.00808v1
• Date: Thu, 1 Apr 2021 23:41:41 GMT
• Title: Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation
• Authors: Subhankar Roy, Evgeny Krivosheev, Zhun Zhong, Nicu Sebe, Elisa Ricci
• Abstract summary: We identify two key aspects that can help to alleviate multiple domain-shifts in the multi-target domain adaptation (MTDA) We propose Curriculum Graph Co-Teaching (CGCT) that uses a dual classifier head, with one of them being a graph convolutional network (GCN) which aggregates features from similar samples across the domains. When the domain labels are available, we propose Domain-aware Curriculum Learning (DCL), a sequential adaptation strategy that first adapts on the easier target domains, followed by the harder ones.
• Score: 78.28390172958643
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper we address multi-target domain adaptation (MTDA), where given one labeled source dataset and multiple unlabeled target datasets that differ in data distributions, the task is to learn a robust predictor for all the target domains. We identify two key aspects that can help to alleviate multiple domain-shifts in the MTDA: feature aggregation and curriculum learning. To this end, we propose Curriculum Graph Co-Teaching (CGCT) that uses a dual classifier head, with one of them being a graph convolutional network (GCN) which aggregates features from similar samples across the domains. To prevent the classifiers from over-fitting on its own noisy pseudo-labels we develop a co-teaching strategy with the dual classifier head that is assisted by curriculum learning to obtain more reliable pseudo-labels. Furthermore, when the domain labels are available, we propose Domain-aware Curriculum Learning (DCL), a sequential adaptation strategy that first adapts on the easier target domains, followed by the harder ones. We experimentally demonstrate the effectiveness of our proposed frameworks on several benchmarks and advance the state-of-the-art in the MTDA by large margins (e.g. +5.6% on the DomainNet).

Related papers

• HYLDA: End-to-end Hybrid Learning Domain Adaptation for LiDAR Semantic Segmentation [13.87939140266266]
  This paper addresses the problem of training a LiDAR semantic segmentation network using a fully-labeled source dataset and a target dataset that only has a small number of labels. We develop a novel image-to-image translation engine, and couple it with a LiDAR semantic segmentation network, resulting in an integrated domain adaptation architecture we call HYLDA.
  arXiv  Detail & Related papers  (2022-01-14T18:13:09Z)
• Reiterative Domain Aware Multi-target Adaptation [14.352214079374463]
  We propose Reiterative D-CGCT (RD-CGCT) that obtains better adaptation performance by reiterating multiple times over each target domain. RD-CGCT significantly improves the performance over D-CGCT for Office-Home and Office31 datasets.
  arXiv  Detail & Related papers  (2021-08-26T17:12:25Z)
• Cross-domain Contrastive Learning for Unsupervised Domain Adaptation [108.63914324182984]
  Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source domain to a different unlabeled target domain. We build upon contrastive self-supervised learning to align features so as to reduce the domain discrepancy between training and testing sets.
  arXiv  Detail & Related papers  (2021-06-10T06:32:30Z)
• Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation [74.71931918541748]
  We propose an instance affinity based criterion for source to target transfer during adaptation, called ILA-DA. We first propose a reliable and efficient method to extract similar and dissimilar samples across source and target, and utilize a multi-sample contrastive loss to drive the domain alignment process. We verify the effectiveness of ILA-DA by observing consistent improvements in accuracy over popular domain adaptation approaches on a variety of benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-03T01:33:14Z)
• Your Classifier can Secretly Suffice Multi-Source Domain Adaptation [72.47706604261992]
  Multi-Source Domain Adaptation (MSDA) deals with the transfer of task knowledge from multiple labeled source domains to an unlabeled target domain. We present a different perspective to MSDA wherein deep models are observed to implicitly align the domains under label supervision.
  arXiv  Detail & Related papers  (2021-03-20T12:44:13Z)
• Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation [32.95273803359897]
  Multi-source unsupervised domain adaptation(MSDA) aims at adapting models trained on multiple labeled source domains to an unlabeled target domain. We propose a novel multi-source domain adaptation framework based on collaborative learning for semantic segmentation.
  arXiv  Detail & Related papers  (2021-03-08T12:51:42Z)
• Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources [68.31273535702256]
  We propose a novel instance-level MDA framework, named curriculum cycle-consistent generative adversarial network (C-CycleGAN) C-CycleGAN consists of three components: (1) pre-trained text encoder which encodes textual input from different domains into a continuous representation space, (2) intermediate domain generator with curriculum instance-level adaptation which bridges the gap across source and target domains, and (3) task classifier trained on the intermediate domain for final sentiment classification. We conduct extensive experiments on three benchmark datasets and achieve substantial gains over state-of-the-art DA approaches.
  arXiv  Detail & Related papers  (2020-11-17T14:50:55Z)
• Class Conditional Alignment for Partial Domain Adaptation [10.506584969668792]
  Adrial adaptation models have demonstrated significant progress towards transferring knowledge from a labeled source dataset to an unlabeled target dataset. PDA investigates the scenarios in which the source domain is large and diverse, and the target label space is a subset of the source label space. We propose a multi-class adversarial architecture for PDA.
  arXiv  Detail & Related papers  (2020-03-14T23:51:57Z)
• Multi-source Domain Adaptation for Visual Sentiment Classification [92.53780541232773]
  We propose a novel multi-source domain adaptation (MDA) method, termed Multi-source Sentiment Generative Adversarial Network (MSGAN) To handle data from multiple source domains, MSGAN learns to find a unified sentiment latent space where data from both the source and target domains share a similar distribution. Extensive experiments conducted on four benchmark datasets demonstrate that MSGAN significantly outperforms the state-of-the-art MDA approaches for visual sentiment classification.
  arXiv  Detail & Related papers  (2020-01-12T08:37:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.